CS Mathematics

Discrete mathematics, graph theory, combinatorics, linear algebra, and the calculus behind ML optimisation. Includes probability and statistics for machine learning. Every topic is chosen because it is directly useful to a software engineer or ML practitioner.

CombinatoricsTopics 11–20

·Counting Principles
·Permutations
·Combinations & Binomial
·Pigeonhole Principle
·Inclusion-Exclusion
·Recurrences
·Master Theorem
·Probability Foundations
·Bayes & Conditional Probability
·Distributions & ML Statistics

Start Combinatorics →

Linear AlgebraTopics 31–40

·Vectors & Norms
·Matrices
·Matrix Operations
·Linear Transformations
·Dot Product & Similarity
·Systems of Equations
·Eigenvalues & Eigenvectors
·SVD & Dimensionality
·Vector Spaces
·Linear Algebra in ML

Start Linear Algebra →

Calculus & OptimisationTopics 41–50

·Limits & Continuity
·Derivatives
·Differentiation Rules
·Partial Derivatives
·The Gradient
·Chain Rule & Backprop
·Gradient Descent
·Loss Functions
·Optimisers: Adam & Beyond
·Convexity & Local Minima

Start Calculus & Optimisation →

Discrete MathTopics 1–10

·Sets
·Logic & Propositions
·Proof Techniques
·Relations
·Functions
·Modular Arithmetic
·Prime Numbers & GCD
·Induction
·Boolean Algebra
·Formal Logic & SAT

Start Discrete Math →

Graph TheoryTopics 21–30

·Graph Fundamentals
·Graph Representations
·Trees
·Traversals: BFS & DFS
·Shortest Paths
·Spanning Trees
·Directed Graphs & DAGs
·Graph Connectivity
·Graph Coloring
·Network Flow

Start Graph Theory →